XAR

Author(s):  
Naveen Ashish ◽  
Sharad Mehrotra

The authors present the XAR framework that allows for free text information extraction and semantic annotation. The language underpinning XAR, the authors argue, allows for the inclusion of probabilistic reasoning with the rule language, provides higher level predicates capturing text features and relationships, and defines and supports advanced features such as token consumption and stratified negotiation in the rule language and semantics. The XAR framework also allows the incorporation of semantic information as integrity constraints in the extraction and annotation process. The XAR framework aims to fill in a gap, the authors claim, in the Web based information extraction systems. XAR provides an extraction and annotation framework by permitting the integrated use of hand-crafted extraction rules, machine-learning based extractors, and semantic information about the particular domain of interest. The XAR system has been deployed in an emergency response scenario with civic agencies in North America and in a scenario with an IT department of a county level community clinic.

2014 ◽  
Vol 539 ◽  
pp. 434-437
Author(s):  
Yu Si Zhang ◽  
Liu Liu ◽  
Ti Yong Zhang

This paper analyzes the deficiencies of the existing Web information extraction methods and reasons, and put forward the page text information extraction method based on multi-feature fusion. Compared with previous methods with a small selection of features, the method in this paper determine the choice of a variety of information via text features, better able to adapt to a variety of styles page. By comparing the experiment, this method has higher accuracy to meet practical application needs in the Web Content Extraction.


2021 ◽  
Vol 2 ◽  
pp. 1-7
Author(s):  
Evangelos Papadias ◽  
Margarita Kokla ◽  
Eleni Tomai

Abstract. A growing body of geospatial research has shifted the focus from fully structured to semistructured and unstructured content written in natural language. Natural language texts provide a wealth of knowledge about geospatial concepts, places, events, and activities that needs to be extracted and formalized to support semantic annotation, knowledge-based exploration, and semantic search. The paper presents a web-based prototype for the extraction of geospatial entities and concepts, and the subsequent semantic visualization and interactive exploration of the extraction results. A lightweight ontology anchored in natural language guides the interpretation of natural language texts and the extraction of relevant domain knowledge. The approach is applied on three heterogeneous sources which provide a wealth of spatial concepts and place names.


Author(s):  
Tércio de Morais Sampaio Silva ◽  
Frederico Luiz Gonçalves de Freitas ◽  
Rafael Cobra Teske ◽  
Guilherme Bittencourt

2004 ◽  
Vol 37 (5) ◽  
pp. 977-997 ◽  
Author(s):  
Keechul Jung ◽  
Kwang In Kim ◽  
Anil K. Jain

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